We refer to our unsupervised flow (combining the feature alignment and aggregation steps) on ER3 as counting grid aggregation (CGA) for TCG based video imprint and epitome aggregation (EPA) for epitome based video imprint. Table REF  shows the retrieval performance compared with baselines. We evaluate the CGA and EPA on two different CNN models, AlexNet [1]} and ResNet-50 [2]}. Figure REF  shows the retrieval performance for each event class of EVVE dataset. The IDs of events are the same with [3]}. We can see that the video representation based on ResNet-50 demonstrates superior performance in most event categories compared with AlexNet. And the proposed aggregation methods can further boost the retrieval performance.
